In competitive running and other personal recreational applications, portable electronic devices may track the location and speed of a user wearing or carrying the device. In conventional devices of this type, the user's speed while running or performing other activities can often be tracked using positioning platforms, including global positioning satellite (GPS) tracking systems, by dividing elapsed distance by elapsed time. Sometimes, however, signals from GPS satellites can be obscured by trees, buildings, weather, and other obstructions. When GPS signals are corrupted or unavailable, the user will often find it convenient to continue to receive an indication of speed using alternative techniques.
An alternative technique that provides a speed value for a runner or other user detects the stride frequency of that user's walking or running pace. It has been known to use an accelerometer built into a portable electronic device to detect the comparatively sharp acceleration at the start of the user's forward stride. By tracking successive strides, the user's stride frequency can be determined.
In known platforms, the stride frequency can be used as an input to a linear, polynomial, or other deterministic model to generate an estimated current speed of the user's walking or running activity. In known estimators of this kind, the device assumes that the user's speed is related to the frequency of their detected strides in a deterministic or closed-form fashion. Sometimes there are different deterministic models for a walking user and a running user. The deterministic models can be based on human biomechanical models or other calculations which assume a direct correspondence between the stride frequency observed for the user and the user's immediate speed at that stride frequency.
However, empirical use of known speed estimator devices has shown that the estimated speed output obtained from these types of deterministic algorithms does not always match up well with actual speed. A user's estimated speed using deterministic models can differ by forty percent or more when compared to the speed as measured by accurate GPS reception.
There is an opportunity to develop methods and systems for speed estimation, in which an estimation of a user's speed while running or performing other activities can be generated with comparatively high accuracy even when GPS or other location services are not available.